activity
20182020
most citedA Comparative Analysis of Forecasting Financial Time Series Using ARIMA, LSTM, and BiLSTM

96 citations · 117 across the 4 of their papers we have counts for

collaborators

5 papers

econ.GN2020

A Concern Analysis of FOMC Statements Comparing The Great Recession and The COVID-19 Pandemic

Luis Felipe Gutiérrez, Sima Siami-Namini, Neda Tavakoli +1

It is important and informative to compare and contrast major economic crises in order to confront novel and unknown cases such as the COVID-19 pandemic. The 2006 Great Recession a…

cs.LG202012 cited

Clustering Time Series Data through Autoencoder-based Deep Learning Models

Neda Tavakoli, Sima Siami-Namini, Mahdi Adl Khanghah +2

Machine learning and in particular deep learning algorithms are the emerging approaches to data analysis. These techniques have transformed traditional data mining-based analysis r…

cs.CR20199 cited

The Performance of Machine and Deep Learning Classifiers in Detecting Zero-Day Vulnerabilities

Faranak Abri, Sima Siami-Namini, Mahdi Adl Khanghah +2

The detection of zero-day attacks and vulnerabilities is a challenging problem. It is of utmost importance for network administrators to identify them with high accuracy. The highe…

cs.LG201996 cited

A Comparative Analysis of Forecasting Financial Time Series Using ARIMA, LSTM, and BiLSTM

Sima Siami-Namini, Neda Tavakoli, Akbar Siami Namin

Machine and deep learning-based algorithms are the emerging approaches in addressing prediction problems in time series. These techniques have been shown to produce more accurate r…

cs.LG2018

Forecasting Economics and Financial Time Series: ARIMA vs. LSTM

Sima Siami-Namini, Akbar Siami Namin

Forecasting time series data is an important subject in economics, business, and finance. Traditionally, there are several techniques to effectively forecast the next lag of time s…